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Recommendation systems aim to provide users with relevant suggestions, but often lack interpretability and fail to capture higher-level semantic relationships between user behaviors and profiles.
Language models are few-shot learners
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S3-rec: Self-supervised learning for sequential recommendation with mutual information maximization
Zhou, K.; Wang, H.; Zhao, W. X.; Zhu, Y.; Wang, S.; Zhang, F.; Wang, Z.; and Wen, J.-R. 2020 · 1902
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The movielens datasets: History and context
Harper, F. M.; and Konstan, J. A. 2015 · 2015
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Image-based recommendations on styles and substitutes
McAuley, J.; Targett, C.; Shi, Q.; and Van Den Hengel, A. 2015 · 2015
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Bert: Pre-training of deep bidirectional transformers for language understanding
Devlin, J.; Chang, M.-W.; Lee, K.; and Toutanova, K. 2018 · 2018
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Self-attentive sequential recommendation
Kang, W.-C.; and McAuley, J. 2018 · 2018
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Improving language understanding by generative pre-training
Radford, A.; Narasimhan, K.; Salimans, T.; Sutskever, I.; et al. 2018 · 2018
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Language models are unsupervised multitask learners
Radford, A.; Wu, J.; Child, R.; Luan, D.; Amodei, D.; Sutskever, I.; et al. 2019 · 2019
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BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer
Sun, F.; Liu, J.; Wu, J.; Pei, C.; Lin, X.; Ou, W.; and Jiang, P. 2019 · 2019
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Knowledge-aware graph neural networks with label smoothness regularization for recommender systems
Wang, H.; Zhang, F.; Zhang, M.; Leskovec, J.; Zhao, M.; Li, W.; and Wang, Z. 2019 · 2019
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Session-based recommendation with graph neural networks
Wu, S.; Tang, Y.; Zhu, Y.; Wang, L.; Xie, X.; and Tan, T. 2019 · 2019
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Feature-level Deeper Self-Attention Network for Sequential Recommendation
Zhang, T.; Zhao, P.; Liu, Y.; Sheng, V. S.; Xu, J.; Wang, D.; Liu, G.; Zhou, X.; et al. 2019 · 2019
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Disentangled graph collaborative filtering
Wang, X.; Jin, H.; Zhang, A.; He, X.; Xu, T.; and Chua, T.-S. 2020 · 2020
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Temporal meta-path guided explainable recommendation
Chen, H.; Li, Y.; Sun, X.; Xu, G.; and Yin, H. 2021 · 2021
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Graph infomax adversarial learning for treatment effect estimation with networked observational data
Chu, Z.; Rathbun, S. L.; and Li, S. 2021 · 2021
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A survey on representation learning for user modeling
Li, S.; and Zhao, H. 2021 · 2021
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Knowledge-guided article embedding refinement for session-based news recommendation
Sheu, H.-S.; Chu, Z.; Qi, D.; and Li, S. 2021 · 2021
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Constrained language models yield few-shot semantic parsers
Shin, R.; Lin, C. H.; Thomson, S.; Chen, C.; Roy, S.; Platanios, E. A.; Pauls, A.; Klein, D.; Eisner, J.; and Van Durme, B. 2021 · 2021
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A survey on causal inference
Yao, L.; Chu, Z.; Li, S.; Li, Y.; Gao, J.; and Zhang, A. 2021 · 2021
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HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequences
Xue, S.; Shi, X.; Zhang, Y. J.; and Mei, H. 2022 · 2022
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Tiny-Attention Adapter: Contexts Are More Important Than the Number of Parameters
Zhao, H.; Tan, H.; and Mei, H. 2022 · 2022
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Causal effect estimation: Recent advances, challenges, and opportunities
Chu, Z.; Huang, J.; Li, R.; Chu, W.; and Li, S. 2023 · 2023
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Ensemble Modeling with Contrastive Knowledge Distillation for Sequential Recommendation
Du, H.; Yuan, H.; Zhao, P.; Zhuang, F.; Liu, G.; Zhao, L.; Liu, Y.; and Sheng, V. S. 2023 · 2023
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Robustness of Learning from Task Instructions
Gu, J.; Zhao, H.; Xu, H.; Nie, L.; Mei, H.; and Yin, W. 2023 · 2023
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Chowdhery, A.; Narang, S.; Devlin, J.; Bosma, M.; Mishra, G.; Roberts, A.; Barham, P.; Chung, H. W.; Sutton, C.; Gehrmann, S.; et al. 2022 · 2022
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Hierarchical capsule prediction network for marketing campaigns effect
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Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5)
Geng, S.; Liu, S.; Fu, Z.; Ge, Y.; and Zhang, Y. 2022 · 2022
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Personalized recommendation system based on knowledge embedding and historical behavior
Hui, B.; Zhang, L.; Zhou, X.; Wen, X.; and Nian, Y. 2022 · 2022
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Learning Large-scale Universal User Representation with Sparse Mixture of Experts
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Training language models to follow instructions with human feedback
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Contrastive learning for representation degeneration problem in sequential recommendation
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Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning
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Can Large Language Models Play Text Games Well? Current State-of-the-Art and Open Questions
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LLM-Guided Multi-View Hypergraph Learning for Human-Centric Explainable Recommendation
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